Industrial robots iteratively perform the same works at fixed locations. Field robots perform various works in diverse environments. Those field robots are emerging along with development of robot technology in recent years. As field robots work in diverse environments, there are many problems to be overcome for robot automation. One of principal issues for field robot automation is to solve a problem of robot self-localization. For robot self-localization, there have been proposed the wide extent of sensors and algorithms. A Global Positioning System (GPS) and an Inertial Measurement Unit (IMU) are employed for positioning sensors. A Visual Odometry (VO) algorithm using a camera image and an ICP algorithm using a laser scanner are employed for obtaining a location of a robot. However, a critical positional error may appear because a GPS signal cannot be received from a satellite under the condition of non-line-of-sight. In the case of using IMU and VO algorithms for a long term, errors could be accumulated. Furthermore, an ICP algorithm could cause an error in the case of processing uneven surface data. A SLAM algorithm may be helpful to solve such problems.